Monte Carlo Simulation and Uncertainty Evaluation of Log-facies Classification Based on Petroelastic Properties

The goal of this work is to present a new methodology for log-facies classification. The classification of facies at well location is a key importance step in reservoir modeling: the static reservoir model essentially consists of stochastic simulations which are based on log-facies classification. We propose here a classification based on both petrophysical and acoustic/elastic properties in order to link log-facies to seismic inverted attributes which are often used as soft conditioning data in reservoir simulation. However different sources of uncertainty affect high resolution log-facies classification. We classify the associated uncertainty in two main groups: the uncertainty related to petrophysical curves (porosity and lithological volume fractions) obtained in quantitative log interpretation and the uncertainty associated to elastic properties (velocities or impedances) recovered by rock physics model. For this reason we introduce in the proposed methodology Monte Carlo simulations to generate several realizations of petrophysical and elastic curves and to obtain different log-facies profiles which are used to infer facies probability.